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Unified Monitoring Architecture
A single platform connecting every ore handling system across your operation
Hardware Layer
Purpose-built OEM data devices for each system - the Smartrail Data Device, Trucktrak TDD, Minegaze MDD, dedicated weighing electronics, and TMM payload measurement devices - each designed for the specific demands of its deployment environment and developed entirely in-house.
Edge Layer
All devices perform onboard event validation independently of server connectivity. Loading, tipping, and weighing events are validated locally using sensor fusion before transmission. Vehicle-to-vehicle mesh networking ensures data delivery in areas without fixed Wi-Fi infrastructure.
Server Layer
All validated production events from all systems are received and processed at the server layer, which can be hosted on Accutrak's managed cloud infrastructure or deployed on-premises on client-owned infrastructure, depending on the operation's connectivity and data governance requirements.
Data Repository
All production, tracking, telemetry, and forms data feeds a single consolidated data repository - structured for real-time dashboards and historical reporting at any aggregation level. Every Accutrak application is built on top of this layer, ensuring consistency across all views and reports.
Application Layer
Live dashboards, scheduled reports, push notifications, and multi-device access are delivered through the Insights and Control platform - a single interface for all stakeholders, from underground supervisors to executive management.
Client Data Access
The consolidated data repository is also available directly to the client. Where an operation wishes to pull data into its own systems - for integration with an ERP, a data warehouse, or a custom analytics tool - access is provided through defined data interfaces.
Integration at Every Level — Integration is possible at both the edge and server layers, allowing any third-party ore flow measuring system to feed directly into the Accutrak data repository - regardless of where the data originates or how it is generated. Mine planning data enters via API to enable real-time call vs actual compliance monitoring in-shift. SCADA systems can be connected to bring fixed plant data - skips, winches, crushers, and bin levels - into the same ore flow environment.
Consolidated Ore Flow
A live view of ore movement from stope to stockpile, across all transport modes simultaneously
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What the Platform Consolidates
- Smartrail - trip-by-trip tonnage and loco utilisation across all rail-bound sections, every consist, every level.
- Trucktrak - payload and utilisation for every LHD and underground dump truck, with material type and destination attributed per trip.
- TMM Payload systems - accurate per-load tonnage from onboard or OEM payload measurement devices, automatically linked to trip records for precise production reporting and cross-system reconciliation.
- Conveyor Belt Scales - continuous flow measurement at all key transfer points, bridging the gaps between transport modes.
- Minegaze - open-pit fleet contribution, with loader productivity and haul truck trip counts consolidated alongside underground systems where operations span both environments.
- Roadgaze - road haulage fleet contribution, with trip counts, cycle times, and fleet utilisation for trucks transporting ore on public roads. Route compliance and security events are logged alongside production data, and where weighbridge integration is active, per-trip tonnage is attributed to each truck and destination.
- Third-party data - fixed plant and infrastructure data integrated via SCADA, including skip hoisting cycle data, winch states, crusher throughput, bin levels, and other relevant systems that form part of the ore handling chain but fall outside the Accutrak instrumented fleet.
Reconciliation in Real Time
Because all data streams feed a single production database, cross-system tonnage reconciliation happens continuously throughout the shift, not as a post-shift exercise. Discrepancies between what is loaded at the stope and what arrives at the surface can be identified and investigated within the same shift, not discovered days later during a monthly audit.
Utilisation Insights
From consolidated throughput to root cause - understanding why performance fell short
The consolidated ore flow view tells you what moved through the system and where the shortfall was. Utilisation insights tell you why. Once a throughput gap or bottleneck is visible at the operation or system level, users can drill down into the specific area and examine the underlying activity data that explains the outcome.
After reviewing total underground TMM tonnage against call, users can drill into individual machines, sections, or loading faces to identify the specific causes of underperformance.
Equipment Availability
Time each machine spent in breakdown or off-shift, broken down by asset and shift. Identifies whether tonnage shortfalls were driven by fleet availability rather than operator or system factors.
Late Starts and Early Finishes
Time lost at the beginning and end of shifts before the first trip or after the last. Recurring patterns of late starts at specific loading faces or by specific operators are immediately visible across the shift record.
Excessive Loading Durations
Time spent at the loading face per trip, compared across machines, loaders, and shifts. Extended loading durations indicate loader inefficiency, machine positioning problems, or fragmentation issues that reduce overall fleet productivity.
Excessive Tipping Durations
Time spent at the tip per trip. Extended tipping durations indicate congestion at the tip, mechanical difficulty, or operator behaviour issues that extend cycle time without adding productive distance.
Queuing and Waiting Time
Time machines spend stationary at loading faces or tips, waiting for access. Queuing reveals over-trucking at loaders or tip congestion - system bottlenecks that reduce effective fleet output regardless of individual machine performance.
Empty Run Times
Travel time and distance while running empty, compared against loaded travel. Extended empty run times indicate suboptimal routing, unnecessary return distances, or machines repositioning between loading faces - time that generates no production.
Payload and Underloading
Where TMM Payload systems are linked, per-load tonnage is available alongside trip records. Consistently underloaded trucks reduce effective tonnes per trip without reducing cycle time - a hidden throughput loss that is invisible without payload data.
Surface fleet utilisation follows the same drill-down model. After reviewing total open-pit throughput, users can isolate underperformance by loader, haul route, or truck fleet and examine the underlying activity.
Equipment Availability
Breakdown time and off-road periods per machine and shift. Surface fleets often have higher availability targets than underground - the platform shows actual availability against those benchmarks at machine level.
Cycle Time Analysis
Full cycle breakdown per trip - loading, haul loaded, tipping, haul empty, and spot time at loader. Segment-by-segment comparison across machines, shifts, and haul routes identifies exactly where time is being lost in the cycle.
Loader Match Factor
Over and under-trucking at each loader, visible in real time and in historical review. Too many trucks at a loader creates queuing. Too few leaves the loader idle. The platform quantifies the imbalance at both individual loader and fleet level.
Queuing and Spot Time
Time trucks spend waiting at loading faces and dumps. Excessive spot time and queue lengths indicate congestion or loader inefficiency and are tracked per loader, per shift, and per machine across the full haul fleet.
Payload and Underloading
Where TMM Payload systems are linked, actual tonnes per load are compared against target payload for each machine and loader. Trucks consistently leaving the loader underloaded represent a direct tonnage loss per trip.
Road haulage utilisation follows the same drill-down model as open-pit fleet management, but with the added dimension of security compliance. After reviewing total road fleet trip counts and cycle times against expectations, users can isolate underperformance by truck, route, or shift and examine the underlying activity. Route deviation and security events are recorded alongside production data, providing a complete picture of how each truck's shift time was used.
Vehicle Availability
Breakdown and off-road periods per truck and per shift. Road fleets are often subject to third-party maintenance schedules and contractor arrangements - the platform records actual availability against shift expectations at individual vehicle level.
Cycle Time Analysis
Full trip cycle breakdown per load — loading duration, loaded haul time, tipping duration, and empty return. Road haul distances are typically longer and more variable than open-pit cycles; segment-by-segment comparison across trucks, shifts, and routes identifies where time is being added to the cycle.
Queuing at Loading and Tipping Points
Time trucks spend stationary at the loading point or tip, waiting for access. Queuing at road haulage tipping facilities is common where multiple fleets or operations converge. Excessive queue time is tracked per truck, per shift, and per tip destination.
Late Starts and Early Finishes
Time lost at shift boundaries — late departures from the loading point and early returns before shift end. Recurring patterns by specific trucks or drivers across shifts are visible in the trip timeline, without relying on manual reporting.
Empty Run Times and Return Efficiency
Travel time while running empty, compared against loaded haul time. On longer road routes, inefficient empty returns — due to unnecessary stops, sub-optimal routing, or extended rest periods — reduce the number of trips a truck can complete per shift without generating any production.
Route Compliance and Security Events
Route deviations, unauthorised standstills, and unapproved tipping events are logged per trip with GPS coordinates, timestamps, and duration. Recurring compliance failures by specific trucks or on specific routes are identifiable across any time range, supporting both operational and security investigations.
Payload and Tonnes per Trip
Where in-motion weighbridge data is integrated, accurate per-trip tonnage is linked directly to each truck's route and trip record. Trucks consistently loading below target represent a direct loss of tonnes per trip - identifiable by truck, by loading point, and by shift across the full road fleet.
Rail-bound production follows defined routes with consistent loading and tipping points. Utilisation drill-down on Smartrail reveals exactly where in the tramming cycle time is being lost and which assets or sections are driving the shortfall.
Loco Availability
Breakdown and off-shift time per loco and per shift. Rail operations are sensitive to individual loco availability - a single breakdown on a critical level can significantly reduce overall shift output, and the platform surfaces these events immediately.
Loading Durations at Ore Passes
Time each consist spends at the ore pass or loading point per trip, compared across levels and shifts. Extended loading durations indicate ore pass hang-ups, fragmentation problems, or chute restrictions that reduce the number of trips achievable per shift.
Tipping Durations
Time spent at the tip per trip. Extended tipping durations at the crusher or main tip indicate congestion or mechanical difficulty and directly reduce the number of trips a loco can complete in a shift.
Late Trip Starts and Shift Gaps
Gaps between trips within a shift, late first-trip departures, and early last-trip returns - all tracked per loco and per level. Idle time clustered around shift changes or meal breaks is immediately visible in the trip timeline.
Payload per Axle, Hopper and Train
Smartrail weighs each consist at axle, hopper, and full train level as an inherent part of the product. Underloaded hoppers and consists are identified per trip, per level, and per shift - distinguishing between ore pass availability constraints and loading practice issues.
Conveyor utilisation insights show not just total tonnes but how the belt was used - revealing periods of underloading, idle running, and flow rate shortfalls that reduce effective throughput below design capacity.
Flow Rate vs Design Capacity
Actual tonnes per hour compared against the belt's design capacity throughout the shift. Periods where the belt ran significantly below capacity indicate feed rate problems at the source - a loader not keeping pace, an ore pass running low, or a transfer point restriction.
Idle Running Time
Time the belt ran with no material load - motor running, belt moving, zero throughput. Idle running consumes energy and maintenance life without producing output. The platform quantifies idle running per belt, per shift, and across the shift timeline.
Downtime and Stop Events
Belt stops, trip events, and unplanned downtime logged with timestamp and duration. Repeated short stops at the same time each shift indicate a feed side constraint. Extended stops indicate mechanical issues that warrant maintenance investigation.
Transfer Point Bottlenecks
Where multiple belts are instrumented, flow rate comparison across transfer points identifies where material is accumulating or where a downstream belt is constraining upstream throughput - making the bottleneck visible across the full conveyor system.
